





"""simple docstring"""





# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {}
SCREAMING_SNAKE_CASE__   =  {}
SCREAMING_SNAKE_CASE__   =  {}


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		type				,		SCREAMING_SNAKE_CASE	:		Optional[str]				,		SCREAMING_SNAKE_CASE	:		Optional[List[str]] = None				,		):







      '''simple docstring'''




      lowerCAmelCase    		=			aliases if aliases is not None else []
      if format_type in _FORMAT_TYPES:
            logger.warning(
                F'Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'     )
      lowerCAmelCase    		=			formatter_cls
      for alias in set(aliases + [format_type]     ):
            if alias in _FORMAT_TYPES_ALIASES:
                  logger.warning(
                      F'Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'     )
            lowerCAmelCase    		=			format_type


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Exception				,		SCREAMING_SNAKE_CASE	:		Optional[str]				,		SCREAMING_SNAKE_CASE	:		Optional[List[str]] = None     ):







      '''simple docstring'''




      lowerCAmelCase    		=			aliases if aliases is not None else []
      for alias in set(aliases + [format_type]     ):
            lowerCAmelCase    		=			unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
       from .torch_formatter import TorchFormatter

       _register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
       SCREAMING_SNAKE_CASE__   =  ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
       _register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
       from .tf_formatter import TFFormatter

       _register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
       SCREAMING_SNAKE_CASE__   =  ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
       _register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
       from .jax_formatter import JaxFormatter

       _register_formatter(JaxFormatter, "jax", aliases=[])
else:
       SCREAMING_SNAKE_CASE__   =  ValueError("JAX needs to be installed to be able to return JAX arrays.")
       _register_unavailable_formatter(_jax_error, "jax", aliases=[])


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[str]     ):







      '''simple docstring'''




      if format_type in _FORMAT_TYPES_ALIASES:
            return _FORMAT_TYPES_ALIASES[format_type]
      else:
            return format_type






def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[str]				,		**SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			get_format_type_from_alias(SCREAMING_SNAKE_CASE     )
      if format_type in _FORMAT_TYPES:
            return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE     )
      if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
            raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
      else:
            raise ValueError(
                F'Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None     )}, but got \'{format_type}\''     )













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RobertaPreLayerNormForCausalLM",
           "RobertaPreLayerNormForMaskedLM",
           "RobertaPreLayerNormForMultipleChoice",
           "RobertaPreLayerNormForQuestionAnswering",
           "RobertaPreLayerNormForSequenceClassification",
           "RobertaPreLayerNormForTokenClassification",
           "RobertaPreLayerNormModel",
           "RobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRobertaPreLayerNormForCausalLM",
           "TFRobertaPreLayerNormForMaskedLM",
           "TFRobertaPreLayerNormForMultipleChoice",
           "TFRobertaPreLayerNormForQuestionAnswering",
           "TFRobertaPreLayerNormForSequenceClassification",
           "TFRobertaPreLayerNormForTokenClassification",
           "TFRobertaPreLayerNormMainLayer",
           "TFRobertaPreLayerNormModel",
           "TFRobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FlaxRobertaPreLayerNormForCausalLM",
           "FlaxRobertaPreLayerNormForMaskedLM",
           "FlaxRobertaPreLayerNormForMultipleChoice",
           "FlaxRobertaPreLayerNormForQuestionAnswering",
           "FlaxRobertaPreLayerNormForSequenceClassification",
           "FlaxRobertaPreLayerNormForTokenClassification",
           "FlaxRobertaPreLayerNormModel",
           "FlaxRobertaPreLayerNormPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






